Bars to Handlebars
The Effect of Micromobility on DUIs
Old Dominion University
Goucher College
Introduction
In general, transportation in the United States is dominated by automobile. However, recent innovations (e.g. rideshare) have provided alternative options that may reduce vehicle dependency. One of the most recent of these innovations is micromobility.
- For example: Boston has conventional “docked” bike-share called Bluebike (launched in 2011) and “dockless” bike-share and e-scooters via Lime (launched in 2018 and 2019).
According to the Bureau of Transportation Statistics, micromobility options have been in over 350 cities across the United States.
Literature
The effects of alternative transportation options on impaired driving outcomes is a topic that has received quite a bit of attention (Fell et al. 2020). Rideshare, in particular, accounts for a significant portion of this interest over the past half-decade.
- Most research to date tends to agree that rideshare has marginally reduced alcohol-related driving fatalities (Dills and Mulholland 2018; Anderson and Davis 2021; Peck 2017; Burton 2021)
- Zhou (2020) used BRFSS to measure the effect of rideshare on binge drinking (increase), drinking days (no change), and drunk driving incidents (no change).
Literature
Little research exists about micromobility, however. Button, Frye, and Reaves (2020) is, to our knowledge, the only micromobility paper published in an economics journal.
That said, the two closest papers to our topic are:
Research Question
In this study, we ask how the introduction of micromobility impacts DUI arrests. We hypothesize that, like rideshare, micromobility could provide an alternative to driving (an automobile) under the influence.
Data
- Bureau of Transportation Statistics
- City-by-year micromobility information
- Start/end months for docked, dockless, and e-scooter systems
- Federal Bureau of Investigation, Crime Data Explorer
- Agency-by-month counts of particular crimes
- Agencies and BTS cities were then matched to one another
- Final dataset covers 268 agencies from 2010 to 2019
- Docked Bikeshare: 82 (30.6% of agencies)
- Dockless Bikeshare: 74 (27.6% of agencies)
- E-Scooters: 78 (29.1% of agencies)
- Uber entry dates and ACS information for each agency
Data
Rollout of Micromobility (agency by time; red indicates treatment)
Data
Rollout of Micromobility is Driven by Population (500K)
Data
| Num. Micromobility Options | 0 | 1 | 2 | 3 |
|---|---|---|---|---|
| Num. Cities/Agencies | 129 | 174 | 67 | 16 |
| Docked | Dockless | Scooter | |
|---|---|---|---|
| Docked | 0.0 | 1.2 | |
| Dockless | 17.6 | 14.9 | |
| Scooter | 39.7 | 29.5 |
Data
Data
Method
We begin by estimating a Poisson regression of the form:
\[\text{DUI}_{at} = \color{red}{\delta} M_{at} + X_{at}\beta + \alpha_a + \tau_t + \epsilon_{at}\]
- where:
- \(M_{at}\) is an indicator variable equal to one if the agency operates in an area with micromobility options.
- \(\alpha_a\) and \(\tau_t\) represent agency and time (year-month) fixed effects.
- \(X_{at}\) is a vector of controls including arrests for drunkenness, liquor law violations, Uber entry, and demographic information (income, age, percent white, education).
- Outcomes currently include arrests for DUIs and Drug Possession (falsification).
- Variations include state-by-year and agency-by-month fixed effects.
Method
We also use the new Local Projections Difference-in-Differences (Dube et al. 2023) estimator to investigate the effects of micromobility.
\[\text{DUI}_{a,t+h} - \text{DUI}_{a,t-1} = \color{red}{\delta_h^{LP}}\Delta M_{a,t} + \tau_{t}^{h} + e_{at}^{h}\]
using only observations where \(\Delta M_{at} = 1\) (newly treated) or \(M_{a,t+h} = 0\) (clean control). This is akin to the “stacking” estimator in Cengiz et al. (2019).
Results - Event Study, Any
Results - Event Study, Any
Results - Event Study, Docked
Results - Event Study, Docked
Results - LPDiD, Docked
Results - LPDiD, Docked
Results
We estimate a \(\approx\) 10% reduction in DUIs per month due to the introduction of micromobility.
While this may seem like an attractive headline, the interpretation is delicate and unlike that of rideshare. A reduction of DUIs indicates less drunk driving, but the actual safety implications are much less clear.
Future Directions
- Coupling these results with those for health outcomes (HCUP, FARS, crashes)
- What happens when micromobility is removed?
- Changes in DUI laws? DUI vs RUI? Micromobility laws can be unclear.
Summary
- We supply one of the first papers to examine the effects of micromobility.
- Using TWFE and LPDiD, we find relatively large reductions in DUI arrests.
- Ideally, we could observe injuries or non-fatal outcomes.
- There’s more work to be done on micromobility!
Bibliography
Virginia Association of Economists (2024)